Abstract Large‐scale artificial intelligence (AI) models such as ChatGPT have the potential to improve performance on many benchmarks and real‐world tasks. However, it is difficult to develop and maintain these models because of their complexity and resource requirements. As a result, they are still inaccessible to healthcare industries and clinicians. This situation might soon be…
MedComm – Future Medicine Template
Write in a clean editor, then format for MedComm – Future Medicine in one click — DocuGuru applies the official Wiley template with author–year references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the MedComm – Future Medicine format
MedComm – Future Medicine is a peer-reviewed journal published by Wiley, covering Diverse Scientific and Economic Studies, Human auditory perception and evaluation, Educational Robotics and Engineering.
| Publisher | Wiley |
|---|---|
| Reference style | Author–year (Chicago) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." MedComm – Future Medicine 12 (3): 45–58.
Formats any DOI in MedComm – Future Medicine style. No sign-up. |
| Publishes research in | Diverse Scientific and Economic Studies Human auditory perception and evaluation Educational Robotics and Engineering Cancer Immunotherapy and Biomarkers COVID-19 Clinical Research Studies |
| ISSN | 2769-6456 |
| Citation impact (2-yr) | 2.1 |
| h-index | 11 |
| i10-index | 18 |
| Total citations | 728 |
| Open access | Yes |
| Top institutions publishing here | Macau University of Science and Technology |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in MedComm – Future Medicine per year
Citation impact of MedComm – Future Medicine by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in MedComm – Future Medicine
Abstract Recent works have shown that Transformer's excellent performances on natural language processing tasks can be maintained on natural image analysis tasks. However, the complicated clinical settings in medical image analysis and varied disease properties bring new challenges for the use of Transformer. The computer vision and medical engineering communities have devoted significant effort to…
Abstract Given the unprecedented phenomenon of population ageing, studies have increasing captured the heterogeneity within the ageing process. In this context, the concept of “biological age” has been introduced as an integrated measure reflecting the individualized ageing pace. Identifying reliable and robust biomarkers of age is critical for the accurate risk stratification of individuals and…
The coronavirus disease 2019 (COVID-19) is a global infectious disease aroused by RNA virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Patients may suffer from severe respiratory failure or even die, posing a huge challenge to global public health. Retinoic acid-inducible gene I (RIG-I) is one of the major pattern recognition receptors, function to recognize…
Abstract Protein structure prediction (PSP) has been a prominent topic in bioinformatics and computational biology, aiming to predict protein function and structure from sequence data. The three‐dimensional conformation of proteins is pivotal for their intricate biological roles. With the advancement of computational capabilities and the adoption of deep learning (DL) technologies (especially Transformer network architectures),…